gen_videos_proj_withseg.py

# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.

"""Generate lerp videos using pretrained network pickle."""

import os
import re
from typing import List, Optional, Tuple, Union

import click
import dnnlib
import imageio
import numpy as np
import scipy.interpolate
import torch
from tqdm import tqdm
import mrcfile
from PIL import Image

import legacy

from camera_utils import LookAtPoseSampler
from torch_utils import misc
#----------------------------------------------------------------------------

def save_image(img, name, output_dir):
    os.makedirs(output_dir, exist_ok=True)  # Create the output directory if it doesn't exist

    image_path = os.path.join(output_dir, f"video_frame_{name}.png")  # Define the path and filename for the image

    img = (img + 1) / 2  # Convert the image from [-1, 1] range to [0, 1] range
    img = img.permute(1, 2, 0)  # Rearrange the dimensions of the image tensor
    img = (img * 255).clamp(0, 255).byte()  # Scale the image values to [0, 255] and convert to byte tensor

    if img.shape[-1] == 1:  # Grayscale image with single channel
        img = img.squeeze(dim=1)  # Remove the single channel channel dimension
        PIL_image = Image.fromarray(img.cpu().numpy(), mode='L')  # Convert to PIL image with 'L' mode (grayscale)
    elif img.shape[-1] == 3:  # RGB image with three channels
        PIL_image = Image.fromarray(img.cpu().numpy(), mode='RGB')  # Convert to PIL image with 'RGB' mode
    else:
        # For images with more than 3 channels, convert to RGBA format by adding an alpha channel
        img = torch.cat((img, torch.full_like(img[..., :1], 255, dtype=torch.uint8)), dim=-1)
        PIL_image = Image.fromarray(img.cpu().numpy(), mode='RGBA')  # Convert to PIL image with 'RGBA' mode

    PIL_image.save(image_path)  # Save the PIL image to disk


def layout_grid(img, grid_w=None, grid_h=1, float_to_uint8=True, chw_to_hwc=True, to_numpy=True):
    batch_size, channels, img_h, img_w = img.shape
    if grid_w is None:
        grid_w = batch_size // grid_h
    assert batch_size == grid_w * grid_h
    if float_to_uint8:
        img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)
    img = img.reshape(grid_h, grid_w, channels, img_h, img_w)
    img = img.permute(2, 0, 3, 1, 4)
    img = img.reshape(channels, grid_h * img_h, grid_w * img_w)
    if chw_to_hwc:
        img = img.permute(1, 2, 0)
    if to_numpy:
        img = img.cpu().numpy()
    return img

def create_samples(N=256, voxel_origin=[0, 0, 0], cube_length=2.0):
    # NOTE: the voxel_origin is actually the (bottom, left, down) corner, not the middle
    voxel_origin = np.array(voxel_origin) - cube_length/2
    voxel_size = cube_length / (N - 1)

    overall_index = torch.arange(0, N ** 3, 1, out=torch.LongTensor())
    samples = torch.zeros(N ** 3, 3)

    # transform first 3 columns
    # to be the x, y, z index
    samples[:, 2] = overall_index % N
    samples[:, 1] = (overall_index.float() / N) % N
    samples[:, 0] = ((overall_index.float() / N) / N) % N

    # transform first 3 columns
    # to be the x, y, z coordinate
    samples[:, 0] = (samples[:, 0] * voxel_size) + voxel_origin[2]
    samples[:, 1] = (samples[:, 1] * voxel_size) + voxel_origin[1]
    samples[:, 2] = (samples[:, 2] * voxel_size) + voxel_origin[0]

    num_samples = N ** 3

    return samples.unsqueeze(0), voxel_origin, voxel_size

#----------------------------------------------------------------------------

def gen_interp_video(G, mp4: str, ws, w_frames=60*4, kind='cubic', grid_dims=(1,1), num_keyframes=None, wraps=2, psi=1, truncation_cutoff=14, cfg='FFHQ', image_mode='image', gen_shapes=False, gen_frames=False, iso_level=10.0, device=torch.device('cuda'), **video_kwargs):
    grid_w = grid_dims[0]
    grid_h = grid_dims[1]

    if num_keyframes is None:
        if len(ws) % (grid_w*grid_h) != 0:
            raise ValueError('Number of input seeds must be divisible by grid W*H')
        num_keyframes = len(ws) // (grid_w*grid_h)

    camera_lookat_point = torch.tensor([0, 0, 0.2], device=device) if cfg == 'FFHQ' else torch.tensor([0, 0, 0], device=device)

    cam2world_pose = LookAtPoseSampler.sample(3.14/2, 3.14/2, camera_lookat_point, radius=2.7, device=device)
    intrinsics = torch.tensor([[4.2647, 0, 0.5], [0, 4.2647, 0.5], [0, 0, 1]], device=device)
    c = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)
    c = c.repeat(len(ws), 1)
    # ws = G.mapping(z=zs, c=c, truncation_psi=psi, truncation_cutoff=truncation_cutoff)
    _ = G.synthesis(ws[:1], c[:1]) # warm up
    ws = ws.reshape(grid_h, grid_w, num_keyframes, *ws.shape[1:])


    # create new folder
    outdirs = os.path.dirname(mp4)
    os.makedirs(outdirs, exist_ok=True)
    # add delta_c
    z_samples = np.random.RandomState(123).randn(10000, G.z_dim)
    delta_c = G.t_mapping(torch.from_numpy(np.mean(z_samples, axis=0, keepdims=True)).to(device), c[:1], truncation_psi=1.0, truncation_cutoff=None, update_emas=False)
    delta_c = torch.squeeze(delta_c, 1)
    c[:,3] += delta_c[:,0]
    c[:,7] += delta_c[:,1]
    c[:,11] += delta_c[:,2]

    # Interpolation.
    grid = []
    for yi in range(grid_h):
        row = []
        for xi in range(grid_w):
            x = np.arange(-num_keyframes * wraps, num_keyframes * (wraps + 1))
            y = np.tile(ws[yi][xi].cpu().numpy(), [wraps * 2 + 1, 1, 1])
            interp = scipy.interpolate.interp1d(x, y, kind=kind, axis=0)
            row.append(interp)
        grid.append(row)

    # Render video.
    max_batch = 10000000
    voxel_resolution = 512
    video_out = imageio.get_writer(mp4, mode='I', fps=60, codec='libx264', **video_kwargs)


    all_poses = []
    for frame_idx in tqdm(range(num_keyframes * w_frames)):
        imgs = []
        for yi in range(grid_h):
            for xi in range(grid_w):
                if cfg == "Head":
                    cam2world_pose = LookAtPoseSampler.sample(3.14/2 + 2 * 3.14 * frame_idx / (num_keyframes * w_frames), 3.14/2,
                                                            camera_lookat_point, radius=2.75, device=device)
                else:
                    pitch_range = 0.25
                    yaw_range = 1.5 # 0.35
                    cam2world_pose = LookAtPoseSampler.sample(3.14/2 + yaw_range * np.sin(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
                                                            3.14/2 -0.05 + pitch_range * np.cos(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
                                                            camera_lookat_point, radius=2.7, device=device)

                all_poses.append(cam2world_pose.squeeze().cpu().numpy())
                intrinsics = torch.tensor([[4.2647, 0, 0.5], [0, 4.2647, 0.5], [0, 0, 1]], device=device)
                c = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)

                interp = grid[yi][xi]
                w = torch.from_numpy(interp(frame_idx / w_frames)).to(device)
                # img = G.synthesis(ws=w.unsqueeze(0), c=c[0:1], noise_mode='const')[image_mode][0]
                
                # fix delta_c
                c[:,3] += delta_c[:,0]
                c[:,7] += delta_c[:,1]
                c[:,11] += delta_c[:,2]
                img = G.synthesis(ws=w.unsqueeze(0), c=c[0:1], noise_mode='const')[image_mode][0]

                if image_mode == 'image_depth':
                    img = -img
                    img = (img - img.min()) / (img.max() - img.min()) * 2 - 1

                imgs.append(img)

                if gen_frames:
                    save_image(img, frame_idx, mp4.replace('.mp4', '/'))

                if gen_shapes and frame_idx == 0:
                    # generate shapes
                    print('Generating shape for frame %d / %d ...' % (frame_idx, num_keyframes * w_frames))
                    
                    samples, voxel_origin, voxel_size = create_samples(N=voxel_resolution, voxel_origin=[0, 0, 0], cube_length=G.rendering_kwargs['box_warp'])
                    samples = samples.to(device)
                    sigmas = torch.zeros((samples.shape[0], samples.shape[1], 1), device=device)
                    transformed_ray_directions_expanded = torch.zeros((samples.shape[0], max_batch, 3), device=device)
                    transformed_ray_directions_expanded[..., -1] = -1

                    head = 0
                    with tqdm(total = samples.shape[1]) as pbar:
                        with torch.no_grad():
                            while head < samples.shape[1]:
                                torch.manual_seed(0)
                                sigma = G.sample_mixed(samples[:, head:head+max_batch], transformed_ray_directions_expanded[:, :samples.shape[1]-head], w.unsqueeze(0), truncation_psi=psi, noise_mode='const')['sigma']
                                sigmas[:, head:head+max_batch] = sigma
                                head += max_batch
                                pbar.update(max_batch)

                    sigmas = sigmas.reshape((voxel_resolution, voxel_resolution, voxel_resolution)).cpu().numpy()
                    sigmas = np.flip(sigmas, 0)
                    
                    pad = int(30 * voxel_resolution / 256)
                    pad_top = int(38 * voxel_resolution / 256)
                    sigmas[:pad] = 0
                    sigmas[-pad:] = 0
                    sigmas[:, :pad] = 0
                    sigmas[:, -pad_top:] = 0
                    sigmas[:, :, :pad] = 0
                    sigmas[:, :, -pad:] = 0

                    output_ply = True
                    if output_ply:
                        from shape_utils import convert_sdf_samples_to_ply
                        convert_sdf_samples_to_ply(np.transpose(sigmas, (2, 1, 0)), [0, 0, 0], 1, mp4.replace('.mp4', '.ply'), level=iso_level)
                    else: # output mrc
                        with mrcfile.new_mmap(mp4.replace('.mp4', '.mrc'), overwrite=True, shape=sigmas.shape, mrc_mode=2) as mrc:
                            mrc.data[:] = sigmas

        video_out.append_data(layout_grid(torch.stack(imgs), grid_w=grid_w, grid_h=grid_h))
    video_out.close()
    all_poses = np.stack(all_poses)

    if gen_shapes:
        print(all_poses.shape)
        with open(mp4.replace('.mp4', '_trajectory.npy'), 'wb') as f:
            np.save(f, all_poses)

#----------------------------------------------------------------------------

def parse_range(s: Union[str, List[int]]) -> List[int]:
    '''Parse a comma separated list of numbers or ranges and return a list of ints.

    Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
    '''
    if isinstance(s, list): return s
    ranges = []
    range_re = re.compile(r'^(\d+)-(\d+)$')
    for p in s.split(','):
        m = range_re.match(p)
        if m:
            ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
        else:
            ranges.append(int(p))
    return ranges

#----------------------------------------------------------------------------

def parse_tuple(s: Union[str, Tuple[int,int]]) -> Tuple[int, int]:
    '''Parse a 'M,N' or 'MxN' integer tuple.

    Example:
        '4x2' returns (4,2)
        '0,1' returns (0,1)
    '''
    if isinstance(s, tuple): return s
    m = re.match(r'^(\d+)[x,](\d+)$', s)
    if m:
        return (int(m.group(1)), int(m.group(2)))
    raise ValueError(f'cannot parse tuple {s}')

#----------------------------------------------------------------------------

@click.command()
@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
@click.option('--latent', type=str, help='latent code', required=True)
@click.option('--output', help='Output path', type=str, required=True)
@click.option('--grid', type=parse_tuple, help='Grid width/height, e.g. \'4x3\' (default: 1x1)', default=(1,1))
@click.option('--num-keyframes', type=int, help='Number of seeds to interpolate through.  If not specified, determine based on the length of the seeds array given by --seeds.', default=None)
@click.option('--w-frames', type=int, help='Number of frames to interpolate between latents', default=240)
@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
@click.option('--trunc-cutoff', 'truncation_cutoff', type=int, help='Truncation cutoff', default=14, show_default=True)
@click.option('--reload_modules', help='Overload persistent modules?', type=bool, required=False, metavar='BOOL', default=False, show_default=True)
@click.option('--cfg', help='Config', type=click.Choice(['FFHQ', 'Cats', 'Head']), required=False, metavar='STR', default='FFHQ', show_default=True)
@click.option('--image_mode', help='Image mode', type=click.Choice(['image', 'image_depth', 'image_raw']), required=False, metavar='STR', default='image', show_default=True)
@click.option('--sample_mult', 'sampling_multiplier', type=float, help='Multiplier for depth sampling in volume rendering', default=1, show_default=True)
@click.option('--nrr', type=int, help='Neural rendering resolution override', default=None, show_default=True)
@click.option('--shapes', type=bool, help='Gen shapes for shape interpolation', default=False, show_default=True)
@click.option('--level', type=float, help='Iso surface level for mesh generation', default=10, show_default=True)
@click.option('--frames', type=bool, help='Save frames as images', default=False, show_default=True)
@click.option('--interpolate', type=bool, help='Interpolate between seeds', default=True, show_default=True)

def generate_images(
    network_pkl: str,
    latent: str,
    output: str,
    truncation_psi: float,
    truncation_cutoff: int,
    grid: Tuple[int,int],
    num_keyframes: Optional[int],
    w_frames: int,
    reload_modules: bool,
    cfg: str,
    image_mode: str,
    sampling_multiplier: float,
    nrr: Optional[int],
    shapes: bool,
    level: float,
    frames: bool,
    interpolate: bool,
):
    """Render a latent vector interpolation video.

    """

    print('Loading networks from "%s"...' % network_pkl)
    device = torch.device('cuda')
    with dnnlib.util.open_url(network_pkl) as f:
        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore



    G.rendering_kwargs['depth_resolution'] = int(G.rendering_kwargs['depth_resolution'] * sampling_multiplier)
    G.rendering_kwargs['depth_resolution_importance'] = int(G.rendering_kwargs['depth_resolution_importance'] * sampling_multiplier)
    if nrr is not None: G.neural_rendering_resolution = nrr

    if truncation_cutoff == 0:
        truncation_psi = 1.0 # truncation cutoff of 0 means no truncation anyways
    if truncation_psi == 1.0:
        truncation_cutoff = 14 # no truncation so doesn't matter where we cutoff

    ws = torch.tensor(np.load(latent)['w']).to(device)
    gen_interp_video(G=G, mp4=output, ws=ws, bitrate='100M', grid_dims=grid, num_keyframes=num_keyframes, w_frames=w_frames, psi=truncation_psi, truncation_cutoff=truncation_cutoff, cfg=cfg, image_mode=image_mode, gen_shapes=shapes, iso_level=level, gen_frames=frames, device=device)

#----------------------------------------------------------------------------

if __name__ == "__main__":
    generate_images() # pylint: disable=no-value-for-parameter

#----------------------------------------------------------------------------